| Paper: | ITT-P4.6 | ||
| Session: | Biomedical and biometric applications | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Industry Technology Track: Biomedical | ||
| Title: | DETECTION OF NEWBORNS' EEG SEIZURE USING TIME-FREQUENCY DIVERGENCE MEASURES | ||
| Authors: | Pega Zarjam; Queensland University of Technology | ||
| Ghasem Azemi; Queensland University of Technology | |||
| Mostefa Mesbah; Queensland University of Technology | |||
| Boualem Boashash; Queensland University of Technology | |||
| Abstract: | In this paper, a time-frequency approach for detecting seizure activities in newborns’ Electroencephalogram (EEG) data is proposed. In this approach, the discrimination between seizure and non-seizure states is based on the time-frequency distance between the consequent segments in the EEG signal. Three different time-frequency measures and three different reduced time-frequency distributions are used in this study. The proposed method is tested on the EEG data acquired from three neonates with ages ranging from two days to two weeks. The experimental results validate the suitability of the proposed method in automated newborns' seizure detection. The results show an average seizure detection rate of 96% and false alarm of 5%. | ||
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